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  <div class="headertitle"><div class="title">PopulationBasedStepSizeAdaptation.h</div></div>
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<a href="_population_based_step_size_adaptation_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * \brief       Implements the tep size adaptation based on the success of the new population compared to the old</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \author    O.Krause</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \date        2014</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> *</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> *</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> *</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> *</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> */</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_DIRECTSEARCH_OPERATORS_POPULATION_BASED_STEP_SIZE_ADAPTATION_H</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="preprocessor">#define SHARK_ALGORITHMS_DIRECTSEARCH_OPERATORS_POPULATION_BASED_STEP_SIZE_ADAPTATION_H</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span> </div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="preprocessor">#include &lt;<a class="code" href="_base_8h.html">shark/LinAlg/Base.h</a>&gt;</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="preprocessor">#include &lt;cmath&gt;</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span> </div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment"></span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="comment">/// \brief Step size adaptation based on the success of the new population compared to the old</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="comment">///</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="comment">/// This is the step size adaptation algorithm as proposed in </span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="comment">/// Ilya Loshchilov, &quot;A Computationally Efficient Limited Memory CMA-ES for Large Scale Optimization&quot;</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="comment">///</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="comment">/// It ranks the old and new population together and checks whether the mean rank of the new population</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment">/// is lower than the old one in this combined population. If this is true, the step size is increased</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">/// in an exponential fashion. More formally, let \f$ r_t(i) \f$ be the rank of the i-th individual in the </span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">/// current population in the combined ranking and  \f$ r_{t-1}(i) \f$ the rank of the i-th previous</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">/// individual. Then we have</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">/// \f[ z_t \leftarrow \frac 1 {\lamba^2} \sum_i^{\lambda} r_{t-1}(i) - r_t(i) - z*\f]</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// where \f$ z* \f$ is a target success value, which defaults to 0.25</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// this statistic is stabilised using an exponential average:</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// \f[ s_t \leftarrow (1-c)*s_{t-1} + c*z_t \f]</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// where the learning rate c defaults to 0.3</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// finally we adapt the step size sigma by</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// \f[ \sigma_t = \sigma_{t-1} exp(s_t/d) \f]</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// where the damping factor d defaults to 1</span></div>
<div class="foldopen" id="foldopen00053" data-start="{" data-end="};">
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html">   53</a></span><span class="comment"></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_population_based_step_size_adaptation.html" title="Step size adaptation based on the success of the new population compared to the old.">PopulationBasedStepSizeAdaptation</a>{</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a639fa5d70bd1c62301f305243a5f2f37">   55</a></span>    <a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a639fa5d70bd1c62301f305243a5f2f37">PopulationBasedStepSizeAdaptation</a>():<a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a63212ec0dcd18e0b0b5539ac841921ad">m_targetSuccessRate</a>(0.25), <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a3c6dadb4074bae9cf45e91d01f44c251">m_c</a>(0.3), <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#ab49e73e5dd7254ff6842aba045df622e">m_d</a>(1.0){}</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    <span class="comment"></span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">    /////Getter and Setter functions/////////</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment"></span>        </div>
<div class="foldopen" id="foldopen00059" data-start="{" data-end="}">
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a427adce1df5a15d76d80b2a19a10fb25">   59</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a427adce1df5a15d76d80b2a19a10fb25">targetSuccessRate</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a63212ec0dcd18e0b0b5539ac841921ad">m_targetSuccessRate</a>;</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>    }</div>
</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>    </div>
<div class="foldopen" id="foldopen00063" data-start="{" data-end="}">
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a706211697c48dd351ade4bbcf281313e">   63</a></span>    <span class="keywordtype">double</span>&amp; <a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a706211697c48dd351ade4bbcf281313e">targetSuccessRate</a>(){</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a63212ec0dcd18e0b0b5539ac841921ad">m_targetSuccessRate</a>;</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>    }</div>
</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>    </div>
<div class="foldopen" id="foldopen00067" data-start="{" data-end="}">
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a79add20d49ef3b80a2d462859eec8f61">   67</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a79add20d49ef3b80a2d462859eec8f61">learningRate</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a3c6dadb4074bae9cf45e91d01f44c251">m_c</a>;</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>    }</div>
</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    </div>
<div class="foldopen" id="foldopen00071" data-start="{" data-end="}">
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a1b9acea64e917b909eec3c2575c36011">   71</a></span>    <span class="keywordtype">double</span>&amp; <a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a1b9acea64e917b909eec3c2575c36011">learningRate</a>(){</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a3c6dadb4074bae9cf45e91d01f44c251">m_c</a>;</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>    }</div>
</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>    </div>
<div class="foldopen" id="foldopen00075" data-start="{" data-end="}">
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a96ca7edda2205c93db4747d00c6d83b9">   75</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a96ca7edda2205c93db4747d00c6d83b9">dampingFactor</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#ab49e73e5dd7254ff6842aba045df622e">m_d</a>;</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>    }</div>
</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>    </div>
<div class="foldopen" id="foldopen00079" data-start="{" data-end="}">
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a118ac8b4dd48146bbe6a113f8bb5ce66">   79</a></span>    <span class="keywordtype">double</span>&amp; <a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a118ac8b4dd48146bbe6a113f8bb5ce66">dampingFactor</a>(){</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#ab49e73e5dd7254ff6842aba045df622e">m_d</a>;</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>    }</div>
</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>    </div>
<div class="foldopen" id="foldopen00083" data-start="{" data-end="}">
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a7b9e246e1aea24500b766d2deecab993">   83</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a7b9e246e1aea24500b766d2deecab993">stepSize</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#ac8eb6144af7204f6855aa751e99481de" title="current value for the step size">m_stepSize</a>;</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>    }</div>
</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>    <span class="comment"></span></div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span><span class="comment">    ///\brief Initializes a new trial by setting the initial learning rate and resetting the internal values.</span></div>
<div class="foldopen" id="foldopen00088" data-start="{" data-end="}">
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a83113ac7f0853d0e7e8fc679816efa6e">   88</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a83113ac7f0853d0e7e8fc679816efa6e" title="Initializes a new trial by setting the initial learning rate and resetting the internal values.">init</a>(<span class="keywordtype">double</span> initialStepSize){</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>        <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#ac8eb6144af7204f6855aa751e99481de" title="current value for the step size">m_stepSize</a> = initialStepSize;</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a07f8ce33e17b3ccf2d6891902fe0ac8c" title="The time average of the population success.">m_s</a> = 0;</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>        <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#af39a53953d0b9fed248b1c8cd5557bd6" title="fitness values of the previous iteration for ranking">m_prevFitness</a>.resize(0);</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    }</div>
</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    <span class="comment"></span></div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span><span class="comment">    /// \brief updates the step size using the newly sampled population</span></div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment">    /// The offspring is assumed to be ordered in ascending order by their penalizedFitness</span></div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment">    /// (this is the same as ordering by the unpenalized fitness in an unconstrained setting)</span></div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Population&gt;</div>
<div class="foldopen" id="foldopen00099" data-start="{" data-end="}">
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a9f4e464bb4dfbf1dee52898069b6570c">   99</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a9f4e464bb4dfbf1dee52898069b6570c" title="updates the step size using the newly sampled population">update</a>(<a class="code hl_typedef" href="_t_s_p_8cpp.html#a64dfcdc1a48639f7e4e3cb79e17f0018">Population</a> <span class="keyword">const</span>&amp; offspring){</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        std::size_t lambda = offspring.size();</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        <span class="keywordflow">if</span> (<a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#af39a53953d0b9fed248b1c8cd5557bd6" title="fitness values of the previous iteration for ranking">m_prevFitness</a>.size() == lambda){</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>            <span class="comment">//get estimate of z</span></div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>            std::size_t indexOld = 0;</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>            std::size_t indexNew = 0;</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>            std::size_t rank = 1;</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>            <span class="keywordtype">double</span> z =  0;</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>            <span class="keywordflow">while</span>(indexOld &lt; lambda &amp;&amp; indexNew &lt; lambda){</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>                <span class="keywordflow">if</span> (offspring[indexNew].<a class="code hl_function" href="namespaceshark.html#a7742884160fe8dc36a05bf3325d28020">penalizedFitness</a>() &lt;= <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#af39a53953d0b9fed248b1c8cd5557bd6" title="fitness values of the previous iteration for ranking">m_prevFitness</a>[indexOld]){</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>                    z-=rank;</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>                    ++indexNew;</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>                }</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>                <span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>                    z+=rank;</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>                    ++indexOld;</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>                }</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>                ++rank;</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>            }</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>            <span class="comment">//case 1: the worst elements in the old population are better than the worst in the new</span></div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>            <span class="keywordflow">while</span>(indexNew &lt; lambda){</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>                z-=rank;</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>                ++indexNew;</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>                ++rank;</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>            }</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>            <span class="comment">//case 2: the opposite</span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>            <span class="keywordflow">while</span>(indexOld&lt; lambda){</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>                z += rank;</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>                ++indexOld;</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>                ++rank;</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>            }</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>            z /= lambda*lambda;</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>            z -= <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a63212ec0dcd18e0b0b5539ac841921ad">m_targetSuccessRate</a>;</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>            <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a07f8ce33e17b3ccf2d6891902fe0ac8c" title="The time average of the population success.">m_s</a> = (1-<a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a3c6dadb4074bae9cf45e91d01f44c251">m_c</a>)*<a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a07f8ce33e17b3ccf2d6891902fe0ac8c" title="The time average of the population success.">m_s</a> +<a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a3c6dadb4074bae9cf45e91d01f44c251">m_c</a>*z;</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>            <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#ac8eb6144af7204f6855aa751e99481de" title="current value for the step size">m_stepSize</a> *= std::exp(<a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a07f8ce33e17b3ccf2d6891902fe0ac8c" title="The time average of the population success.">m_s</a>/<a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#ab49e73e5dd7254ff6842aba045df622e">m_d</a>);</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        }</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        </div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        <span class="comment">//store fitness values of last iteration</span></div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#af39a53953d0b9fed248b1c8cd5557bd6" title="fitness values of the previous iteration for ranking">m_prevFitness</a>.resize(lambda);</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != lambda; ++i)</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>            <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#af39a53953d0b9fed248b1c8cd5557bd6" title="fitness values of the previous iteration for ranking">m_prevFitness</a>(i) = offspring[i].penalizedFitness();</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>    }</div>
</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#ac8eb6144af7204f6855aa751e99481de">  142</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#ac8eb6144af7204f6855aa751e99481de" title="current value for the step size">m_stepSize</a>;<span class="comment">///&lt; current value for the step size</span></div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#af39a53953d0b9fed248b1c8cd5557bd6">  143</a></span>    RealVector <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#af39a53953d0b9fed248b1c8cd5557bd6" title="fitness values of the previous iteration for ranking">m_prevFitness</a>;<span class="comment">///&lt; fitness values of the previous iteration for ranking</span></div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a07f8ce33e17b3ccf2d6891902fe0ac8c">  144</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a07f8ce33e17b3ccf2d6891902fe0ac8c" title="The time average of the population success.">m_s</a>; <span class="comment">///&lt; The time average of the population success</span></div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span> </div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>    <span class="comment">//hyper parameters</span></div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a63212ec0dcd18e0b0b5539ac841921ad">  147</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a63212ec0dcd18e0b0b5539ac841921ad">m_targetSuccessRate</a>;</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#a3c6dadb4074bae9cf45e91d01f44c251">  148</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#a3c6dadb4074bae9cf45e91d01f44c251">m_c</a>;</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"><a class="line" href="classshark_1_1_population_based_step_size_adaptation.html#ab49e73e5dd7254ff6842aba045df622e">  149</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_population_based_step_size_adaptation.html#ab49e73e5dd7254ff6842aba045df622e">m_d</a>;</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>};</div>
</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span> </div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>}</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span> </div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span><span class="preprocessor">#endif</span></div>
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